Data Quality: Empowering Businesses with Analytics and AI (Hardcover) (數據品質：以分析與人工智慧賦能企業)
Southekal, Prashanth
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商品描述
Discover how to achieve business goals by relying on high-quality, robust data
In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications.
The author shows you how to:
- Profile for data quality, including the appropriate techniques, criteria, and KPIs
- Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.
- Formulate the reference architecture for data quality, including practical design patterns for remediating data quality
- Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business
An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
商品描述(中文翻譯)
在《資料品質：透過分析和人工智慧增強企業能力》中，一位經驗豐富的資料和分析專家提供了一個實用且實際操作的討論，介紹如何利用高品質的資料加速業務成果。在這本書中，您將學習到定義和評估資料品質的技巧，發現如何確保公司的資料收集實踐避免常見的問題和缺陷，提高業務中的資料品質水平，並確保所得到的資料能夠用於支持高級分析和人工智慧應用。
作者向您展示了如何：
- 進行資料品質分析，包括適當的技術、標準和關鍵績效指標
- 確定業務中資料品質問題的根本原因，並討論了導致組織資料品質下降的16個常見原因
- 制定資料品質的參考架構，包括解決資料品質問題的實用設計模式
- 實施10個最佳資料品質實踐和改善業務運營、合規性和決策能力所需的能力
《資料品質：透過分析和人工智慧增強企業能力》是資料科學家、資料分析師、商業智能專業人士、首席技術和資料官以及任何對收集和使用高品質資料有興趣的人的重要資源。對於希望了解優質資料與其他資料有何不同的企業領導者來說，這本書也是一本必備之選。
作者簡介
PRASHANTH SOUTHEKAL, PHD, is a data, analytics, and AI consultant, author, and professor. He has worked and consulted for over 80 organizations including P&G, GE, Shell, Apple, FedEx, and SAP. Dr. Southekal is the author of Data for Business Performance and Analytics Best Practices (ranked #1 analytics books of all time by BookAuthority) and writes regularly on data, analytics, and AI in Forbes and CFO.University. He serves on the Editorial Board of MIT CDOIQ Symposium and is an advisory board member at BGV (Benhamou Global Ventures) a Silicon Valley-based venture capital firm. Apart from his consulting and advisory pursuits, he has trained over 3,000 professionals worldwide in data and analytics. Dr. Southekal is also an adjunct professor of data and analytics at IE Business School (Madrid, Spain). CDO Magazine included him in the top 75 global academic data leaders of 2022. He holds a PhD from ESC Lille (FR), an MBA from the Kellogg School of Management (US), and holds the ICD.D designation from the Institute of Corporate Directors (Canada).
作者簡介(中文翻譯)
PRASHANTH SOUTHEKAL, PHD，是一位數據、分析和人工智能顧問、作家和教授。他曾在包括寶潔、通用電氣、殼牌、蘋果、聯邦快遞和SAP在內的80多家組織工作和提供顧問服務。Southekal博士是《Data for Business Performance》和《Analytics Best Practices》（由BookAuthority評選為有史以來最佳分析書籍）的作者，並定期在《福布斯》和CFO.University上撰寫有關數據、分析和人工智能的文章。他是MIT CDOIQ Symposium的編輯委員會成員，也是矽谷風險投資公司BGV（Benhamou Global Ventures）的顧問委員會成員。除了顧問和顧問工作外，他還在全球培訓了超過3,000名專業人士的數據和分析能力。Southekal博士還是IE商學院（西班牙馬德里）的數據和分析兼職教授。《CDO Magazine》將他列為2022年全球頂尖75位學術數據領導者之一。他擁有法國ESC Lille的博士學位，美國Kellogg管理學院的MBA學位，並擁有加拿大公司董事會學會的ICD.D資格。
